The contribution of rapid KATP channel gene mutation analysis to the clinical management of children with congenital hyperinsulinism
Bibliographic record
Abstract
OBJECTIVE: In children with congenital hyperinsulinism (CHI), K(ATP) channel genes (ABCC8 and KCNJ11) can be screened rapidly for potential pathogenic mutations. We aimed to assess the contribution of rapid genetic testing to the clinical management of CHI. DESIGN: Follow-up observational study at two CHI referral hospitals. METHODS: Clinical outcomes such as subtotal pancreatectomy, (18)F-Dopa positron emission tomography-computed tomography (PET-CT) scanning, stability on medical treatment and remission were assessed in a cohort of 101 children with CHI. RESULTS: In total, 32 (32%) children had pathogenic mutations in K(ATP) channel genes (27 in ABCC8 and five in KCNJ11), of which 11 (34%) were novel. In those negative at initial screening, other mutations (GLUD1, GCK, and HNF4A) were identified in three children. Those with homozygous/compound heterozygous ABCC8/KCNJ11 mutations were more likely to require a subtotal pancreatectomy CHI (7/10, 70%). Those with paternal heterozygous mutations were investigated with (18)F-Dopa PET-CT scanning and 7/13 (54%) had a focal lesionectomy, whereas four (31%) required subtotal pancreatectomy for diffuse CHI. Those with maternal heterozygous mutations were most likely to achieve remission (5/5, 100%). In 66 with no identified mutation, 43 (65%) achieved remission, 22 (33%) were stable on medical treatment and only one child required a subtotal pancreatectomy. CONCLUSIONS: Rapid genetic analysis is important in the management pathway of CHI; it provides aetiological confirmation of the diagnosis, indicates the likely need for a subtotal pancreatectomy and identifies those who require (18)F-Dopa PET-CT scanning. In the absence of a mutation, reassurance of a favourable outcome can be given early in the course of CHI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".